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After model-fitting, we often need to convert model objects into a dataframe, either for reporting or for visualizations. 📜
— R Function A Day (@rfunctionaday) May 19, 2021
The {model_parameters} function from the {parameters} 📦 does this for many regression model objects 🌟https://t.co/4jOHsZDCaz#rstats #DataScience pic.twitter.com/uI7sUSF0bz
Run #PyTorch in the Browser using #ONNXJS. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCodehttps://t.co/6j3nYTocJC pic.twitter.com/kAj8yeCZq8
— Dr. Ganapathi Pulipaka 🇺🇸 (@gp_pulipaka) May 19, 2021
thinking of getting an #rstats tattoo...
— Benjamin Gowan (@Benjaming_G) May 19, 2021
conflict_prefer("select", "dplyr")
thoughts? Too much? 😂
---
title: "#rstats Twitter Explorer"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
source_code: embed
theme:
version: 4
bootswatch: yeti
css: styles/main.css
---
```{r setup, include=FALSE}
library(flexdashboard)
library(rtweet)
library(dplyr)
library(httr)
library(lubridate)
library(echarts4r)
get_unique_value <- function(data, col) {
col <- enquo(col)
data %>%
pull(!!col) %>%
unique() %>%
length()
}
rstats_tweets <- read_twitter_csv("data/rstats_tweets.csv")
count_timeseries <- rstats_tweets %>%
ts_data(by = "hours")
tweets_today <- rstats_tweets %>%
filter(created_at == today()-1)
by_hour <- rstats_tweets %>%
group_by(hour = hour(created_at)) %>%
summarise(count = n()) %>%
ungroup()
number_of_unique_tweets <- get_unique_value(rstats_tweets, text)
number_of_unique_tweets_today <- get_unique_value(tweets_today, text)
number_of_tweeters_today <- get_unique_value(tweets_today, user_id)
number_of_likes <- rstats_tweets %>%
pull(favorite_count) %>%
sum()
get_tweet_embed <- function(user, status_id) {
url <- stringr::str_glue("https://publish.twitter.com/oembed?url=https://twitter.com/{user}/status/{status_id}&partner=&hide_thread=false")
response <- GET(url) %>%
content()
return(shiny::HTML(response$html))
}
```
Row
-----------------------------------------------------------------------
### Tweets Today
```{r}
valueBox(number_of_unique_tweets_today, icon = "fa-comment-alt", color = "plum")
```
### Tweeters Today
```{r}
valueBox(number_of_tweeters_today, icon = "fa-user", color = "peachpuff")
```
### #rstats Likes
```{r}
valueBox(number_of_likes, icon = "fa-heart", color = "palevioletred")
```
### #rstats Tweets
```{r}
valueBox(number_of_unique_tweets, icon = "fa-comments", color = "mediumorchid")
```
Row {.tabset .tabset-fade data-width=400}
-----------------------------------------------------------------------
### Tweet volume
```{r}
count_timeseries %>%
e_charts(time) %>%
e_line(n, name = "# of tweets", smooth = TRUE) %>%
e_x_axis(
type = "time",
formatter = htmlwidgets::JS(
"function(value){
let date = new Date(value);
label = `${date.getDate()}-${(parseInt(date.getMonth()) + 1)}-${date.getFullYear()}`;
return label;
}"
)
) %>%
e_axis_labels(y = "Tweets") %>%
e_theme("westeros") %>%
e_tooltip(trigger = "axis", formatter = htmlwidgets::JS("
function(params) {
let date = new Date(params[0].value[0])
let options = { year: 'numeric', month: 'short', day: 'numeric', hour: 'numeric'}
let title = `${date.toLocaleDateString('en-US', options=options)}`
let num = `${params[0].value[1]} tweets`
return(`${title}${num}`);
}"))
```
### Tweets by Hour of Day
```{r}
by_hour %>%
e_charts(hour) %>%
e_step(count, name = "Tweets", step = "middle") %>%
e_x_axis(
min = 0,
max = 23,
) %>%
e_axis_labels(x = "Time of Day (UTC)", y = "Tweets") %>%
e_theme("westeros") %>%
e_tooltip(trigger = "axis", formatter = htmlwidgets::JS("
function(params) {
let title = `${params[0].value[0]}h`
let num = `${params[0].value[1]} tweets`
return(`${title}${num}`);
}"))
```
Row
-----------------------------------------------------------------------
### 💗 Most Liked Tweet Today {.tweet-box}
```{r}
most_liked_url <- tweets_today %>%
slice_max(favorite_count)
get_tweet_embed(most_liked_url$screen_name, most_liked_url$status_id)
```
### ✨ Most Retweeted Tweet Today {.tweet-box}
```{r}
most_retweeted <- tweets_today %>%
slice_max(retweet_count)
get_tweet_embed(most_retweeted$screen_name, most_retweeted$status_id)
```
### 🎉 Most Recent {.tweet-box}
```{r}
most_recent <- tweets_today %>%
slice_max(created_at)
get_tweet_embed(most_recent$screen_name[1], most_recent$status_id[1])
```